Staff AI Engineer

Functional Responsibilities:

• Partner with client executives to translate ambiguous business problems into enterprise AI solution architectures with clear trade-off analyses (cost, latency, risk).
• Design and build scalable backend systems, data pipelines, and APIs integrating LLMs, agentic workflows,and RAGs.
• Implement multi-agent orchestration frameworks and advanced retrieval mechanisms (vector DBs, hybrid search) for complex workflows.
• Deploy and manage cloud-native AI applications across AWS, GCP, Azure, or Databricks using Docker, Kubernetes, Terraform, and CI/CD pipelines.
• Instrument systems with LLM telemetry, cost-tracking, security guardrails, and systematic evaluation harnesses (LLM-as-a-judge) to ensure safety and performance.
• Fine-tune prompts and optimize inference latency using caching, quantization, and cost-reduction strategies.
• Serve as the embedded technical authority within client environments to align cross-functional teams and manage technical risks.
• Elevate team standards (modular code, testing, CI/CD) and mentor client technical staff to build long-term operational autonomy

Qualifications:

• 8+ years of experience in Software/ML Engineering, with 3+ years specifically focused on production GenAI/LLM applications (RAG, agents, tool use) and 2+ years in customer-facing or forward-deployed roles.
• Deep hands-on experience building production systems with Generative AI frameworks (LangGraph, LangChain, LlamaIndex, OpenAI, vector databases).
• Proven ability to architect and scale complex backend microservices and APIs using Python (FastAPI, Django, Flask) alongside relational and NoSQL databases.
• Hands-on expertise building, deploying, and managing cloud-native applications on AWS, GCP, Azure, or Databricks using Docker, Kubernetes, Terraform, MLflow, and automated CI/CD pipelines.
• Experience implementing LLM telemetry, cost-tracking, security guardrails, and systematic evaluation harnesses (LLM-as-a-judge patterns).
• Exceptional ability to structure ambiguous client problems into clear technical requirements and present trade-off analyses (cost, latency, risk) to non-technical executive stakeholders.
• Fluent English communication (written and spoken) with a track record of driving engagements independently in fast-paced, high-stakes environments.